Acceleration of conjugate gradient algorithms for unconstrained optimization

Conjugate gradient methods are important for large-scale unconstrained optimization. This paper proposes an acceleration of these methods using a modification of steplength. The idea is to modify in a multiplicative manner the steplength αk, computed by Wolfe line search conditions, by means of a positive parameter ηk, in such a way to improve the behavior of the classical conjugate gradient algorithms. It is shown that for uniformly convex...